Affinity Enrichment for Mass Spectrometry-based Interactomics

Keilhauer et al. (2014) introduced a high-performance affinity enrichment approach for mass spectrometry (AE-MS)- based evaluation of protein-protein interactions.1 The research team contrasted the new methodology with traditional affinity purification (AP-MS), finding that it gave comparable results from a simpler workflow.

In order to study the interactome, researchers must focus on the associations between proteins and specific binders during physiological change without disturbing the complexes during sample preparation. They also need to use a method that is sufficiently sensitive to measure low-abundance interactomes amidst high backgrounds of non-specific binding. For this reason, Keilhauer et al. propose that an affinity enrichment approach offers a robust and simpler solution.

Instead of the traditional approach, which uses sodium dodecyl polyacrylamide gel electrophoresis (SDS-PAGE) to separate the complexes prior to immunological detection by Western blotting, the research team suggests enrichment by automated immunoprecipitation followed by label-free quantitation (LFQ) for mass spectrometry analysis.

Using green fluorescence protein (GFP)-tagged yeast, Saccharomyces cerevisiae, the team constructed cells expressing various bait proteins, each one comprising a different structure. They harvested the cells, then lysed them before immunoprecipitation using robotic magnetic separation based on GFP capture. Following this enrichment step, the team then digested the cell preparations using trypsin before proteomics analysis by liquid chromatography–tandem mass spectrometry (LC-MS/MS). Keilhauer et al. used an EASY-nLC system for chromatographic separation followed by high-resolution (HR) data-dependent spectral data acquisition using an LTQ Orbitrap mass spectrometer (both Thermo Scientific). With reference to the Uniprot yeast proteome database, the team used MaxQuant for quantitation and Perseus for statistical analysis of spectral data.

During the initial steps to establish assay working parameters and optimization, Keilhauer et al. noticed that with the AE workflow under development, pre-fractionation from the initial yeast cell pull-downs was not required. Omitting this step led to faster processing, in addition to identification of approximately half of the entire proteome in each sample. They also found that the magnetic enrichment process produced a highly consistent internal “beadome” of interacting proteins that bound to the separation beads. On closer examination, the researchers saw that this beadome varied according to both protein abundance and affinity to the beads. Moreover, it formed in a highly reproducible manner and was not affected by bait protein structural variations. The internal beadome was thus a consistent and reproducible finding in all experiments. Therefore, instead of discarding this as background, the team was able to incorporate the findings, using them to normalize the data for LFQ and address quality control between runs.

Keilhauer et al. also used this background “noise” to assess enrichment level cutoffs when they examined bait protein interactome data, finding that the precision from the LFQ-AE-HRMS/MS workflow was suitable for characterizing both high- and low-abundance proteins in the interactomes.

The authors describe the method as simple, requiring only HRMS, tagged proteins and software commonly available to most researchers. Furthermore, it is capable of delivering high-quality data on protein interactomes that are comparable with other studies. They also note that the workflow can be adapted to handle samples prepared using SILAC (stable isotope labeling of amino acids in culture).